Pseudo-ct generation from mr data using tissue parameter estimation

ABSTRACT

Systems and methods are provided for generating a pseudo-CT prediction model using multi-channel MR images. An exemplary system may include a processor configured to retrieve training data including multiple MR images and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may determine at least one tissue parameter map based on the multiple MR images and obtain CT values based on the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the tissue parameter maps and the CT values of the plurality of training subjects.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is related to Attorney Docket No.12475.0043-00000 filed Oct. 13, 2015 and titled “Pseudo-CT Generationfrom MR Data Using a Feature Regression Model,” the entire contents ofwhich are incorporated herein by reference.

TECHNICAL FIELD

This disclosure generally relates to radiation therapy or radiotherapy.More specifically, the disclosure relates to systems and methods forgenerating pseudo-CT images from MR data for use in developing aradiation therapy treatment plan to be used during radiotherapy.

BACKGROUND

Radiotherapy is used to treat cancers and other ailments in mammalian(e.g., human and animal) tissue. One such radiotherapy technique is aGamma Knife, by which a patient is irradiated by a large number oflow-intensity gamma rays that converge with high intensity and highprecision at a target (e.g., a tumor). In another embodiment,radiotherapy is provided using a linear accelerator, whereby a tumor isirradiated by high-energy particles (e.g., electrons, protons, ions, andthe like). The placement and dose of the radiation beam must beaccurately controlled to ensure the tumor receives the prescribedradiation, and the placement of the beam should be such as to minimizedamage to the surrounding healthy tissue, often called the organ(s) atrisk (OARs). Radiation is termed “prescribed” because a physician ordersa predefined amount of radiation to the tumor and surrounding organssimilar to a prescription for medicine.

Traditionally, for each patient, a radiation therapy treatment plan(“treatment plan”) may be created using an optimization technique basedon clinical and dosimetric objectives and constraints (e.g., themaximum, minimum, and mean doses of radiation to the tumor and criticalorgans). The treatment planning procedure may include using athree-dimensional image of the patient to identify a target region(e.g., the tumor) and to identify critical organs near the tumor.Creation of a treatment plan can be a time consuming process where aplanner tries to comply with various treatment objectives or constraints(e.g., dose volume histogram (DVH) objectives), taking into accounttheir individual importance (e.g., weighting) in order to produce atreatment plan that is clinically acceptable. This task can be atime-consuming trial-and-error process that is complicated by thevarious organs at risk (OARs) because as the number of OARs increases(e.g., up to thirteen for a head-and-neck treatment), so does thecomplexity of the process. OARs distant from a tumor may be easilyspared from radiation, while OARs close to or overlapping a target tumormay be difficult to spare.

Computed Tomography (CT) imaging traditionally serves as the primarysource of image data for treatment planning for radiation therapy. CTimages offer accurate representation of patient geometry, and CT valuescan be directly converted to electron densities (e.g., Hounsfield units)for radiation dose calculation. However, using CT causes the patient tobe exposed to additional radiation dosage. In addition to CT images,magnetic resonance imaging (MRI) scans can be used in radiation therapydue to their superior soft-tissue contrast, as compared to CT images.MRI is free of ionizing radiation and can be used to capture functionalinformation of the human body, such as tissue metabolism andfunctionality.

Thus, MRI can be used to complement CT for more accurate structurecontouring. However, MRI intensity values are not directly related toelectron densities and cannot be directly used for dose computation;therefore, it is desirable to convert a MR image into a correspondingderived image, usually a CT image (often referred to as a “pseudo-CTimage”). A pseudo-CT image, like a real CT image, has a set of datapoints that indicate CT values that are directly convertible to electrondensities for radiation dose calculation. Thus, a pseudo-CT imagederived from an MR image can be used to facilitate patient dosecomputation in radiation therapy treatment planning. Therefore, it isdesirable to accurately generating a pseudo-CT image using MR image datain order for patients to be spared from additional radiation exposurearising from CT imaging. What is needed is for pseudo-CT images to beable to replace “real” CT images.

Typically, to create pseudo-CT images, atlas images are employed. Anatlas image is a pre-existing image that is used as a reference tofacilitate how a new image is to be translated to generate a derivedimage. For example, in the pseudo-CT image generation context, an atlasMR image and an atlas CT image can be used as references for generatinga derived CT image from a new MR image. Atlas images can be previouslygenerated of the same region of interest for the same patient who is thesubject of the new MR images, where these atlas images have beenanalyzed to identify structures of interest. For example, in manytreatment or diagnostic situations, the patient will need to besubjected to imaging at different times over the course of treatment ordiagnosis. However, this need not always be true, for example, the atlasimages do not need to be images of the same person.

The atlas MR image and the atlas CT image are preferably aligned witheach other via a registration technique (i.e., such that an atlas MRimage and an atlas CT image are “registered” with each other, or are in“registration”). With such registration, a give point in the atlas MRimage for a particular location of the subject can be mapped to a givenpoint in the atlas CT image for the same particular location (and viceversa). However, there may be a certain amount of error that can bepresent in this registration. As such, the registration between theatlas MR and the atlas CT may not be perfect.

In order to replace a real CT image, the pseudo-CT image should be asclose as possible to a real CT image of the patient for the purpose ofdose computation in radiation therapy treatment planning or forgenerating digitally reconstructed radiographs (DRRs) for imageguidance. However, there is not a simple mathematical relationshipbetween CT image intensity values (CT values) and MR intensity values.The difficulty arises because MR intensity values are not standardizedand can vary significantly depending upon different MR scanner settingsor different MR imaging sequence parameters. Thus, existing techniques,such as assigning CT values based on tissue segmentation of an MR imageor those based on point comparison and weighted combination, provideonly a very rough assignment, resulting in existing pseudo-CT imagesthat lack the anatomical details of a true CT image.

Therefore, there is a need for generating pseudo-CT images with improvedquality that are capable of replacing real CT images for the purposes ofdose computation in treatment planning, generating digitallyreconstructed radiographs (DRRs) for image guidance, and the like.

SUMMARY

In one aspect, the present disclosure involves a system for generating apseudo-CT prediction model. The system may include a database configuredto store training data comprising MR data and CT data of a plurality oftraining subjects. Each training subject may have at least one MR imageand at least one CT image. The system may also include a processorcommunicatively coupled to the database for accessing information storedin the database. The system may further include a memory communicativelycoupled to the processor. The memory may store instructions that, whenexecuted by the processor, configures the processor to perform variousoperations. The operations may include accessing the database toretrieve the training data including at least one MR image and at leastone CT image for each of the plurality of training subjects. For eachtraining subject, the operations may include extracting a plurality offeatures from each image point of the at least one MR image, creating afeature vector for each image point based on the extracted features, andextracting a CT value from each image point of the at least one CTimage. The operations may also include generating the pseudo-CTprediction model based on the feature vectors and the CT values of theplurality of training subjects.

In another aspect, the present disclosure involves a system forgenerating a pseudo-CT image. The system may include a processor and amemory communicatively coupled to the processor. The memory may storeinstructions that, when executed by the processor, configures theprocessor to perform various operations. The operations may includereceiving an MR image of a patient and extracting a plurality offeatures from each image point of the MR image. The operations may alsoinclude creating a feature vector for each image point based on theextracted features. The operations may further include determine a CTvalue for each image point based on the feature vector created for thatimage point using a predictive model. In addition, the operations mayinclude generating the pseudo-CT image based on the CT values determinedfor all image points.

In a further aspect, the present disclosure involves a system forgenerating a pseudo-CT prediction image for a patient. The system mayinclude a processor and a memory communicatively coupled to theprocessor. The memory may store instructions that, when executed by theprocessor, configures the processor to perform various operations. Theoperations may include receiving an MR image of the patient andextracting a plurality of features from the MR image. The operations mayalso include generating an intermediate image using a predictive modelbased on the extracted features. The operations may further includeextracting one or more features from the intermediate image. Inaddition, the operations may include generating the pseudo-CT image forthe patient based on the plurality of features extracted from the MRimage and the one or more features extracted from the intermediateimage.

In a further aspect, the present disclosure involves a system forgenerating a pseudo-CT prediction model. The system may include adatabase configured to store training data comprising multi-channel MRdata and CT data of a plurality of training subjects. Each trainingsubject may have multiple MR images and at least one CT image. Thesystem may also include a processor communicatively coupled to thedatabase for accessing information stored in the database. The systemmay further include a memory communicatively coupled to the processor.The memory may store instructions that, when executed by the processor,configures the processor to perform various operations. The operationsmay include accessing the database to retrieve the training dataincluding multiple MR images and at least one CT image for each of theplurality of training subjects. For each training subject, theoperations may include determining at least one tissue parameter mapbased on the multiple MR images and obtaining CT values based on the atleast one CT image. The operations may also include generating thepseudo-CT prediction model based on the tissue parameter maps and the CTvalues of the plurality of training subjects.

In a further aspect, the present disclosure involves a system forgenerating a pseudo-CT image. The system may include a processor and amemory communicatively coupled to the processor. The memory may storeinstructions that, when executed by the processor, configures theprocessor to perform various operations. The operations may includereceiving multiple multi-channel MR images of a patient and convertingthe multiple multi-channel MR images into at least one tissue parametermap. The operations may also include generating CT values by applying apredictive model to the at least one tissue parameter map. Theoperations may further include generating the pseudo-CT image based onthe CT values generated by the predictive model.

In a further aspect, the present disclosure involves acomputer-implemented method for generating a pseudo-CT prediction model.The method may include retrieving training data including multiplemulti-channel MR images and at least one CT image for each of aplurality of training subjects. For each training subject, the methodmay include determining at least one tissue parameter map based on themultiple multi-channel MR images and obtaining CT values based on the atleast one CT image. The method may also include generating the pseudo-CTprediction model based on the tissue parameter maps and the CT values ofthe plurality of training subjects.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed. Theseand other features and advantages of the present disclosure will beapparent to those having ordinary skill in the art upon review of theteachings as described in the following description, drawings andclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate disclosed embodiments and,together with the description and claims, serve to explain the disclosedembodiments. Such embodiments are demonstrative and not intended to beexhaustive or exclusive embodiments of the present apparatuses, systems,or methods. In the drawings, which are not necessarily drawn to scale,like numerals may describe similar components in different views. Likenumerals having letter suffixes or different letter suffixes mayrepresent different instances of similar components.

FIG. 1 is a diagram of an exemplary process for building a pseudo-CTpredictive model.

FIG. 2 is a diagram of an exemplary process for extracting features fromeach voxel of an MR image.

FIG. 3 is a diagram of an exemplary process for using the predictionmodule of FIG. 1 to generate a pseudo-CT image of a patient.

FIG. 4A illustrates an exemplary radiotherapy system.

FIG. 4B illustrates an exemplary radiotherapy device, a Gamma Knife.

FIG. 4C illustrates an exemplary radiotherapy device that is a linearaccelerator.

FIG. 5 illustrates an exemplary system for building a pseudo-CTpredictive model and generating pseudo-CT images.

FIG. 6 is a flowchart of an exemplary process for training and buildinga pseudo-CT predictive model.

FIG. 7 is a flowchart of an exemplary process for using a pseudo-CTpredictive model to generate a pseudo-CT image.

FIG. 8 is a diagram of an exemplary process for training and building amulti-stage pseudo-CT predictive model.

FIG. 9 is a diagram of an exemplary process for using a multi-stagepseudo-CT predictive model to generate a pseudo-CT image.

FIG. 10 is a diagram of an exemplary process for training a pseudo-CTpredictive model using tissue parameter estimated from multi-channel MRscans.

FIG. 11 is a flow chart of an exemplary method of building a pseudo-CTpredictive model using multi-channel MR data.

FIG. 12 is a flowchart of an example method for generating a pseudo-CTimage for a patient using multi-channel MR images.

DETAILED DESCRIPTION

Reference will now be made in detail to the disclosed embodiments,examples of which are illustrated in the accompanying drawings. Whereverconvenient, the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

In one embodiment, in order to create a pseudo-CT image (also referredto as a synthetic CT image or a derived CT image) from an MR image, alearning-based approach that includes a training module and a predictionmodule is provided. The training module constructs a predictive model(also referred to as a regression model) that can be used to predict aCT value for any given voxel based on features extracted from one ormore MR images for a selected location. During training, MR scans and CTscans are collected from a plurality of existing patients to formtraining data. The training data include pairs of pre-aligned CT and MRimages from existing patients. For each pair of images, thecorresponding MR and CT values are known and in registration for everypixel or voxel (also referred to as an image point that includes both 2Dand 3D scenarios).

The predictive model can be trained using the training data. During thetraining phase, regression methods (e.g., statistical learning,regression analysis, or machine learning techniques) can be used on thecollected training data to train the model. After the predictive modelis trained, the model can be used by the prediction module to predictthe CT value for each image point of a patient image. Therefore, thetrained model can be used to create pseudo-CT images from MR data of anyfuture scans, and for the same or a different patent.

FIG. 1 illustrates a flowchart of an exemplary process for building apseudo-CT predictive model 150, consistent with the disclosedembodiments. As illustrated, one embodiment is a learning-based approachthat includes a training module and a prediction module. The trainingmodule creates, in an embodiment, a regression model (e.g., pseudo-CTmodel 150) that can be used by the prediction module to predictpseudo-CT values based on one or more new MR scans.

In one embodiment, training data 110 may be collected from existingpatients or subjects (collectively referred to as “training subjects”).The training subjects may have both MR scans and corresponding CT scanspreviously taken and available to be used to build the pseudo-CTpredictive model 150. Training data 110 may include data for a pluralityof training subjects who have had at least one MR scan and at least oneCT scan (e.g., training subject data 110 a-110 _(N)). The greater numberof training subjects to provide the training data (e.g., the larger thedata set) will typically allow for a better pseudo-CT prediction modelto be generated compared to a model made of a smaller set of data.Training subject data 110 a-110 _(N) includes pairs of pre-aligned MRand CT images. The MR and CT images may be acquired separately;therefore if the images are overlaid upon one another, they typically donot match. So, image registration, as known in the art, is used topre-align the MR and CT images. According to some embodiments, the MRscans associated with the training subjects may be generated by the sameMR scanner as that of MR scan(s) of a new patient for which a pseudo-CTimage is desired. In other embodiments, the MR scans associated with thetraining subjects may be generated by different MR scanners. Further,multiple MR scans associated with a single training subject may includeMR scans of different contrast properties (e.g., T1-weighted,T2-weighted, etc.), to provide more accurate pseudo-CT generationresults.

An image feature extraction module 111 may be used to extract imagefeatures from the MR images associated with the training data 110. Imagefeatures may refer to numerical (e.g., intensity values, coordinatelocations of the feature and the like) or categorical properties (e.g.,a tissue type, a structure label and the like) of an MR voxel. Forexample, an “intensity feature” may refer to the intensity value of anMR voxel. However, any single MR feature may not be enough to adequatelyrepresent the MR voxels for the purposes of generating pseudo-CT images.For example, the intensity value of an MR voxel taken alone provides anambiguous representation for CT estimation. A single intensity value isambiguous because, among other things, two MR voxels of the sameintensity level can belong to different tissues (e.g., bone and air)having different CT values. As used herein, the term “tissue” refers toa classification and is not merely to suggest specific types of tissue;e.g., air is not a tissue. Thus, a plurality of feature types for eachMR voxel of an MR scan are extracted in order to provide a moredistinctive description of the MR voxels.

With multiple MR images or multi-channel MR images, a rich set ofimage-based features can be extracted, which provides more informationand can lead to more accurate pseudo-CT prediction. Image featureextraction module 111 can be used to extract features from each image oreach channel separately (e.g., MR feature vectors 120).

The resulting MR feature vectors 120 may include a plurality sets ofcollected image feature vectors, each associated with a trainingsubject's MR scan(s) (e.g., image vector sets 120 a-120 _(N)). Eachimage feature vector set 120 a-120 _(N) may include a plurality offeature vectors. For example, each column of a given feature vector set(e.g., 120 a) may represent a feature vector, which includes a pluralityof features as vector elements. The plurality of features of a featurevector represent different types of image features associated with, forexample, an image point (e.g., a voxel) of an MR scan/image for atraining subject. The number of the feature elements in a feature vector(e.g., the number of elements in a column) is also referred to thedimension of the feature vector. In some embodiments, feature vectorsmay also be arranged in rows or other suitable forms. Feature extractionaccording to disclosed embodiments is discussed in additional detailbelow with respect to FIG. 2.

FIG. 2 illustrates a feature extraction process consistent with thedisclosed embodiments. A plurality of image features may be extractedfor each MR voxel of an MR scan for a patient (e.g., MR subject a 210).As shown in FIG. 2, the extracted image features may include a localpattern feature 212, a landmark feature 214, a context feature 216, andvarious other types of features 218. Each of these features may berepresented by one or more feature elements, shown as small blockscollectively forming a feature vector column in FIG. 2. The features canbe associated with an image pixel (in 2D), an image voxel (in 3D), or acollection of image points (e.g., an image patch, in 2D or 3D). Forexample, FIG. 2 shows feature vectors (e.g., columns) associated withvoxel i, voxel i+1, . . . , voxel M. A plurality of feature vectors, forexample, a collection of feature vectors associated with multiple voxelsof an MR image (e.g., voxels i to M), may form the feature vector set120 a corresponding to training subject a.

A non-limiting list of potential image features includes:

-   -   Intensity features: MR image intensity values at multiple        scales—either the raw intensity values or after some        pre-processing, such as MR intensity bias correction and/or MR        intensity standardization/normalization;    -   Landmark-based features: the relative location, distance or        other geometric features that are computed for a given voxel        with respect to one or more landmark points (e.g., Anterior        commissure-posterior commissure (AC-PC) points of the brain,        center of each eye ball, and the like);    -   Context features: any other image features that are computed at        certain neighborhood locations of the given point;    -   Location features: the normalized coordinates of each voxel. The        normalization may be accomplished, for example, by aligning each        image to a common reference frame using either linear or        non-linear image registration;    -   Patch features: a patch may refer in some embodiments to a        sub-region or a sub-set of an image surrounding the image voxel        for which the features are computed. For example, a patch may        include 5×5×5 voxels in size, and the image intensity values at        the 125 voxel locations may be associated with 125 feature        elements for the point in the center of the patch;    -   High level features can be derived from one or more patches:        these types of features can include a variety of feature        descriptors known in the art, such as a SIFT (Scale-invariant        feature transform), a SURF (Speeded Up Robust Features), a GLOH        (Gradient Location and Orientation Histogram), a LBP (local        binary patterns), or a HOG (Histogram of Oriented Gradients),        and the like. Such features may be computed for each 2D image        slice that contains a voxel under consideration, and        furthermore, such features may in an embodiment be extended to a        3D image;    -   Texture features: such as energy, entropy, contrast,        homogeneity, and correlation of local image grayscale        co-occurrence matrix, as well as those computed through        filtering the image with Gabor filters, etc.;    -   Joint features: such as when a plurality of MR images (e.g.,        T1-weighted, T2-weighted, etc.) are associated with a given        training subject. In such cases, features such as an intensity,        a patch, a texture, etc., may be extracted from each MR scan        independently for later combination. Additionally, features that        characterized the correlation between the plurality of MR scans        may be computed at each voxel location, e.g., a local joint        histogram, and/or a local cross-correlation, or a co-variance of        multiple MR channels;    -   Features derived from a convolution of images with at least one        linear or non-linear filter (e.g., a local phase, gradients, a        curvature, an edge-detector, or a corner-detectors, and the        like);    -   Features derived by a transformation of the images (e.g., a        Fourier transform, a Hilbert transform, a Radon transform, a        distance transform, a discrete cosine transform, a wavelet        transform, and the like);    -   Region co-variance features: a co-variance of any of the above        point-wise features within a local sub-region; and    -   Classification-based features, discussed more fully below.

As shown in FIG. 2, the collection of features associated with an MRimage voxel may be represented in a single vector (e.g., vectorassociated with voxel i, voxel i+1, . . . , voxel M).

Returning to FIG. 1, upon extraction of the image features, the MRfeature vectors 120 may have multiple dimensions (e.g., each featureelement in a feature vector may be considered as a dimension, as shownin FIG. 2). However, when the number of extracted features from the MRimages increases, the task of creating a predictive model may becomemore difficult to accomplish. This is because each patient imagenormally contains millions of voxels, and each voxel may be associatedwith a large number of features. Therefore, if features extracted fromall voxels of all the images from all the plurality of existing patientsare used to build the predictive model, the computational cost forprocessing such huge amount of data can be very expensive. As a result,the practical number of dimensions depends on the processing power ofthe computer available relative to the computational cost. In addition,the performance of the predictive model resulting from processing theextracted features may not be proportional to the number of featuredimensions. In some cases, as the number of feature dimensionsincreases, the performance of predictive models may decrease because theeffect of one feature may be cancelled or weakened by another feature ifboth are included in the processing. A large number of features may alsocause unacceptable computational costs in using the prediction model todetermine pseudo-CT images based on new MR data. Thus, in an embodiment,a dimensionality reduction module 132 may be used to generate reduceddimension feature vectors 140 without substantial loss of discriminativeinformation provided by the MR features. The dimensionality reductionmodule 132 can be used to capture most of the relevant information froman original feature vector when reducing the original number ofdimensions. For example, some dimensions of the MR feature vectors 120may include noise or other information irrelevant to generatingpseudo-CT images that can be removed. Other dimensions may includeredundant information that can be combined or streamlined for a morecompact representation of the distinctive information provided by thefeatures. For example, if the original data fit a Gaussian distribution,the overall dimension of the original data may be reduced byrepresenting the original data using the mean and standard deviation ofthe original data. Such a dimension reduction method causes the originaldata to be transformed. In some embodiments, the level of reduction indimensionality can range from using the original feature vectors (i.e.,no reduction) to any predetermined level of dimensionality (e.g., areduced set of feature vectors). Thus, in an embodiment, thedimensionality reduction module 132 may be optional and the originalfeature vectors can be used to produce the pseudo-CT model 150.

If the dimensionality reduction module 132 is utilized, dimensionalityreduction techniques used by model 132 may include at least two types oftechniques: (1) unsupervised dimensionality reduction and (2) superviseddimensionality reduction. Typically, supervised dimensionality reductionis better than unsupervised dimensionality reduction, as describedbelow.

Unsupervised dimensionality reduction may remove insignificant noise anddata redundancy and require only MR feature vectors 120 as input. Commonunsupervised dimensionality reduction techniques include, for example,principal component analysis (PCA), and its nonlinear version, kernelprincipal component analysis (KPCA).

Supervised dimensionality reduction may utilize other data of interestto further filter out dimensions irrelevant to generate a pseudo-CTimage. For example, CT values 130 may be used for dimensionalityreduction. CT values 130 (e.g., the original CT values or CT numbers)may be obtained from the CT scan data of training data 110. Superviseddimensionality reduction may take both MR feature vectors 120 and CTvalues 130 as input. Possible supervised dimensionality reductiontechniques include, for example: canonical component analysis (CCA),metric learning (ML) methods, supervised principal component analysis(SPCA), locality sensitive hashing (LSH), local sensitive discriminativeanalysis (LSDA), etc. For dimensionality reduction techniques thatrequire the data of interest to be associated with discrete classlabels, image segmentation may be applied to the CT or MR scans intraining data 110, resulting in segmentation classes that may be used asthe class labels.

CT values 130 may be utilized by the dimensionality reduction module 132to determine what signals in training data 110 are related to theunderlying CT values. Using the original CT values, irrelevant signalscan be suppressed while maintaining relevant signals. In general, atleast one CT image for each training subject should be available. Insome embodiments, multiple CT images may be available. A greater numberof CT scans can be averaged to reduce image noise, thereby improving theeffectiveness of dimensionality reduction module 132. The output of thedimensionality reduction module 132 is a reduced dimension featurevector 140.

Once the training data are collected and processed (e.g., subjected toimage feature extraction, dimensionality reduction techniques, etc.), apseudo-CT prediction model 150 can be built using either statisticallearning or machine learning techniques. In one embodiment, regressionanalysis can be used to build the pseudo-CT prediction model 150.Regression analysis is a statistical process for estimating therelationships among variables. There are a number of known methods toperform regression analysis, for example: linear regression or ordinaryleast squares regression, among others, are “parametric” in that theregression function is defined in terms of a finite number of unknownmodel parameters that can be estimated from training data. For pseudo-CTimage generation, a regression model (e.g., Equation 1) can be defined,for example, as:

H≈f(X,β),  (Equation 1)

where “H” denotes the CT values, “X” denotes a vector of input variables(e.g., any one of MR feature vectors 120 or reduced dimension featurevectors 140), and “β” denotes a vector of unknown parameters to bedetermined or trained for the regression model. In an embodiment, the CTvalues may be Hounsfield values for a CT scan.

Training data 110 that include MR scans and CT scans provide a set ofknown H values (e.g., CT values associated with a training subject's CTscans) having corresponding X values (e.g., feature vectors extractedfrom the MR scans of the same training subject). Using these data, themodel parameter β can be computed using data fitting techniques such asleast squares, maximum likelihood or the like. Once β is estimated, themodel can then compute H (e.g., pseudo-CT values) for a new set of Xvalues (e.g., feature vectors extracted from a new MR scan).

In another embodiment, machine learning and supervised learning can beused for building the pseudo-CT prediction model 150. Supervisedlearning is a branch of machine learning that infers a prediction modelgiven a set of training data. Each sample of the training data is a pairconsisting of input data (e.g., a vector of measurements or features)and a desired output value (also called a supervisory signal). Asupervised learning algorithm analyzes the training data and produces apredictor function, which is a regression function when the outputvariable is numeric or is continuous. Consistent with disclosedembodiments, many different algorithms can be applied, including but notlimited to: kNN (k-nearest neighbors) regression, support vectormachines, neural networks, decision trees, random forests, and gradientboosting machines.

FIG. 3. Illustrates a flowchart of an exemplary process for a predictionmodule that can use the Pseudo-CT model 150, consistent with thedisclosed embodiments. Once the pseudo-CT model 150 is created andtrained, the model 150 can be used by the prediction module 301 in anapplication stage to generate a pseudo-CT image from a new MR scan,either for the same patient or for a new patient. As shown in FIG. 3,the process of generating the pseudo-CT image 350 is similar to theprocess described above for FIG. 1, except that the pseudo-CT predictionmodel 150 previously generated and trained is utilized in theapplication stage. In the process, a new MR scan 310 is input into theprediction module 301. In an embodiment, the prediction module 301 caninclude an image feature extraction module 311 and the pseudo-CTprediction model 150. In this embodiment, the MR scan 301 has nocorresponding CT scan. Features can be extracted from MR scan 301 togenerate patient MR feature vectors 320 in a similar manner to thatdiscussed above with respect to generation of MR feature vectors 120. Adimensionality reduction module 321 can be included to reduce thedimensions of the patient MR feature vectors 320. Alternatively, thepatient MR feature vectors 320 may be used by the pseudo-CT predictionmodel 150 without any reduction in dimensionality, as indicated by dashline 331.

In this way, the prediction module 301 uses the pseudo-CT predictionmodel 150 developed during the training stage to predict a pseudo-CTvalue at each location of the patient MR image 310, as no CT scan wasoriginally provided corresponding to the new MR scan. Because thepseudo-CT prediction model 150 may operate “point-wise”, e.g., at everyimage location, the pseudo-CT value represents a value derived based ona feature vector for a particular voxel at a particular location in theMR scan 310. The prediction model 301 can therefore generate pseudo-CTvalues 340. The pseudo-CT values 340 represent a plurality of intensityvalues for the pseudo-CT image 350. To generate the pseudo-CT image 350,the pseudo-CT values 340 are typically placed into their properlocations on a grid of voxels. In an embodiment, the prediction model301 may predict some values (e.g., pseudo-CT values 340) of the voxelgrid as the image is a grid of voxels (e.g., not every image voxel ispredicted); and then interpolation may be used to generate the pseudo-CTimage 350 to depict an accurate visual representation of the patient'sanatomical details.

The pseudo-CT prediction model 150 can be trained once using trainingdata 110 of all available patients and then the pseudo-CT predictionmodel 150 can be used for all future new patients. Alternatively, thesame pseudo-CT prediction model 150 may not be used for every patient. Apseudo-CT prediction model 150 may be customized for a particularpatient. For example, the training data may be selected to include datamost similar or relevant to the new patient and a model can be builtspecific for the new patient.

FIG. 4A illustrates an exemplary radiotherapy system 400, according tosome embodiments of the present disclosure. Radiotherapy system 400 mayinclude a training module 412, a prediction module 414, a trainingdatabase 422, a testing database 424, a radiotherapy device 430, and animage acquisition device 440. Radiotherapy system 400 may also beconnected to a treatment planning system (TPS) 442 and an oncologyinformation system (01S) 444, which may provide patient information. Inaddition, radiotherapy system 400 may include a display device and auser interface (not shown).

FIG. 4B illustrates an example of one type of radiotherapy device 430(e.g., Leksell Gamma Knife manufactured by Elekta, AB, Stockholm,Sweden), according to some embodiments of the present disclosure. Asshown in FIG. 4B, in a radiotherapy treatment session, a patient 452 maywear a coordinate frame 454 to keep stable the patient's body part(e.g., the head) undergoing surgery or radiotherapy. Coordinate frame454 and a patient positioning system 456 may establish a spatialcoordinate system, which may be used while imaging a patient or duringradiation surgery. Radiotherapy device 430 may include a protectivehousing 464 to enclose a plurality of radiation sources 462. Radiationsources 462 may generate a plurality of radiation beams (e.g., beamlets)through beam channels 466. The plurality of radiation beams may beconfigured to focus on an isocenter 458 from different directions. Whileeach individual radiation beam may have a relatively low intensity,isocenter 458 may receive a relatively high level of radiation whenmultiple doses from different radiation beams accumulate at isocenter458. In certain embodiments, isocenter 458 may correspond to a targetunder surgery or treatment, such as a tumor. The radiotherapy device 430(e.g., Leksell Gamma Knife manufactured by Elekta, AB, Stockholm,Sweden), may, in an embodiment, utilize MR images with assigned bulkdensities, or CT images fused with MR images, and may use pseudo-CTimages generated consistent with the disclosed embodiments.

FIG. 4C illustrates another example of a radiotherapy device 430 (e.g.,a linear accelerator 470), according to some embodiments of the presentdisclosure. Using a linear accelerator 470, a patient 472 may bepositioned on a patient table 473 to receive the radiation dosedetermined by the treatment plan. Linear accelerator 470 may include aradiation head 475 that generates a radiation beam 476. The entireradiation head 475 may be rotatable around a horizontal axis 477. Inaddition, below the patient table 473 there may be provided a flat panelscintillator detector 474, which may rotate synchronously with radiationhead 475 around an isocenter 471. The intersection of the axis 477 withthe center of the beam 476, produced by the radiation head 475, isusually referred to as the isocenter. The patient table 473 may bemotorized so that the patient 472 can be positioned with the tumor siteat or close to the isocenter 471. The radiation head 475 may rotateabout a gantry 478, to provide patient 472 with a plurality of varyingdosages of radiation according to the treatment plan. In an alternativeembodiment, the linear accelerator 470 may be a MR-linear accelerator(“MR-LINAC”). Both the linear accelerator 10 and the MR-LINAC may in anembodiment, utilize MR images, CT images, and may use pseudo-CT imagesgenerated consistent with the disclosed embodiments.

FIG. 5 is an exemplary system 500 for building a pseudo-CT predictivemodel and generating pseudo-CT images, consistent with the disclosedembodiments. According to some embodiments, system 500 may be one ormore high-performance computing devices capable of identifying,analyzing, maintaining, generating, and/or providing large amounts ofdata consistent with the disclosed embodiments. System 500 may bestandalone, or it may be part of a subsystem, which in turn may be partof a larger system. For example, system 500 may represent distributedhigh-performance servers that are remotely located and communicate overa network, such as the Internet, or a dedicated network, such as a LANor a WAN. In some embodiments, system 500 may include an embeddedsystem, MR scanner, and/or touchscreen display device in communicationwith one or more remotely located high-performance computing devices.

In one embodiment, system 500 may include one or more processors 514,one or more memories 510, and one or more communication interfaces 515.Processor 514 may be a processing device, include one or moregeneral-purpose processing devices such as a microprocessor, centralprocessing unit (CPU), graphics processing unit (GPU), or the like. Moreparticularly, processor 514 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction Word (VLIW) microprocessor, aprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 514 may alsobe one or more special-purpose processing devices such as an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), a digital signal processor (DSP), a System on a Chip (SoC), orthe like. As would be appreciated by those skilled in the art, in someembodiments, processor 514 may be a special-purpose processor, ratherthan a general-purpose processor. Processor 514 may include one or moreknown processing devices, such as a microprocessor from the Pentium™ orXeon™ family manufactured by Intel™, the Turion™ family manufactured byAMD™, or any of various processors manufactured by Sun Microsystems.Processor 514 may also include graphical processing units manufacturedby Nvidia™. The disclosed embodiments are not limited to any type ofprocessor(s) otherwise configured to meet the computing demands ofidentifying, analyzing, maintaining, generating, and/or providing largeamounts of imaging data or any other type of data consistent with thedisclosed embodiments.

Memory 510 may include one or more storage devices configured to storecomputer-executable instructions used by processor 514 to performfunctions related to the disclosed embodiments. For example, memory 510may store computer executable software instructions for treatmentplanning software 511, operating system software 512, andtraining/prediction software 513. Processor 514 may be communicativelycoupled to the memory/storage device 510, and the processor 514 may beconfigured to execute the computer executable instructions storedthereon to perform one or more operations consistent with the disclosedembodiments. For example, processor 514 may execute training/predictionsoftware 513 to implement functionalities of training module 412 andprediction module 414. In addition, processor device 514 may executetreatment planning software 511 (e.g., such as Monaco® softwaremanufactured by Elekta) that may interface with training/predictionsoftware 513.

The disclosed embodiments are not limited to separate programs orcomputers configured to perform dedicated tasks. For example, memory 510may include a single program that performs the functions of the system500 or multiple programs (e.g., treatment planning software 511 and/ortraining/prediction software 513). Additionally, processor 514 mayexecute one or more programs located remotely from system 500, such asprograms stored in database 520, such remote programs may includeoncology information system software or treatment planning software.Memory 510 may also store image data or any other type ofdata/information in any format that the system may use to performoperations consistent with the disclosed embodiments.

Communication interface 515 may be one or more devices configured toallow data to be received and/or transmitted by system 500.Communication interface 515 may include one or more digital and/oranalog communication devices that allow system 500 to communicate withother machines and devices, such as remotely located components ofsystem 500, database 520, or hospital database 530. For example,Processor 514 may be communicatively connected to database(s) 520 orhospital database(s) 530 through communication interface 515. Forexample, Communication interface 515 may be a computer network, such asthe Internet, or a dedicated network, such as a LAN or a WAN.Alternatively, the communication interface 515 may be a satellitecommunications link or any form of digital or analog communications linkthat allows processor 514 to send/receive data to/from eitherdatabase(s) 520, 530.

Database(s) 520 and hospital database(s) 530 may include one or morememory devices that store information and are accessed and managedthrough system 500. By way of example, database(s) 520, hospitaldatabase(s) 530, or both may include relational databases such asOracle™ databases, Sybase™ databases, or others and may includenon-relational databases, such as Hadoop sequence files, HBase,Cassandra or others. The databases or other files may include, forexample, raw data from MR scans or CT scans associated with trainingsubjects, MR feature vectors 120, CT values 130, reduced-dimensionfeature vectors 140, pseudo-CT prediction model(s) 150, pseudo-CTvalue(s) 340, pseudo-CT image(s) 350, DICOM data, etc. Systems andmethods of disclosed embodiments, however, are not limited to separatedatabases. In one aspect, system 500 may include database(s) 520 orhospital database(s) 530. Alternatively, database(s) 520 and/or hospitaldatabase(s) 530 may be located remotely from the system 500. Database(s)520 and hospital database(s) 530 may include computing components (e.g.,database management system, database server, etc.) configured to receiveand process requests for data stored in memory devices of database(s)520 or hospital database(s) 530 and to provide data from database(s) 520or hospital database(s) 530.

System 500 may communicate with other devices and components of system500 over a network (not shown). The network may be any type of network(including infrastructure) that provides communications, exchangesinformation, or facilitates the exchange of information and enables thesending and receiving of information between other devices and/orcomponents of system 500 over a network (not shown). In otherembodiments, one or more components of system 500 may communicatedirectly through a dedicated communication link(s), such as a link(e.g., hardwired link, wireless link, or satellite link, or othercommunication link) between system 500 and database(s) 520 and hospitaldatabase(s) 530.

The configuration and boundaries of the functional building blocks ofsystem 500 has been defined herein for the convenience of thedescription. Alternative boundaries can be defined so long as thespecified functions and relationships thereof are appropriatelyperformed. Alternatives (including equivalents, extensions, variations,deviations, etc., of those described herein) will be apparent to personsskilled in the relevant art(s) based on the teachings contained herein.Such alternatives fall within the scope and spirit of the disclosedembodiments.

FIG. 6 is a flowchart of an exemplary process 600 for training andbuilding a pseudo-CT predictive model, consistent with the disclosedembodiments. Process 600 includes a plurality of steps, some of whichmay be optional. At step 610, system 500 may access training data 110associated with a plurality of training subjects from, for example,database 520 and/or hospital database 530. The training data 110 mayinclude at least one MR scan and at least one CT scan for each trainingsubject (e.g., as shown in FIG. 1, training subject data 110 a-110_(N)). In some embodiments, the training data 110 may include at leastone MR scan and multiple CT scans for the same patient.

According to some embodiments, system 500 may determine whether some orall of the training data 110 require preprocessing before being used totrain and build the pseudo-CT prediction model 150. At step 620,processor 514 determines whether the MR scans and the corresponding CTscans for one or more of the training subjects in the training data arealigned (e.g., for each MR voxel, the CT value(s) from the correspondingCT voxel are known). If the MR and CT images are not aligned, process600 follows branch 621 (e.g., “NO”) to align the scans at step 624.System 600 may align the MR scan and corresponding CT scan(s) accordingto methods known to those of skill in the art, as needed. Alternatively,if the MR and CT image(s) are aligned, process 600 follows branch 622(e.g., “YES”) continues to step 630.

Optionally, at step 630, processor 514 verifies if the training data 110include multiple CT scans for the same training subject. If there aremultiple CT scans, then processor 514 determines the average CT valuesfor corresponding CT voxels between the multiple CT scans in order toreduce image noise for the same patient. Otherwise, process 600 proceedsdirectly from step 620 to step 640.

At step 640, as part of pre-processing, processor 514 determines whetherto reduce or eliminate image artifacts from the MR scans based on, forexample, system settings reflected in treatment planning software 511 ortraining/prediction software 513. If reduction of image artifacts isdesired, process 600 follows branch 642 (“YES”) to step 644. At step644, processor 514 may apply, as part of pre-processing, image artifactreduction techniques. By pre-processing the MR scans, processor 514 canremove or reduce image artifacts, such as intensity non-uniformity (alsoknown as MR image bias field) and image noise. In addition,pre-processing can normalize/standardize MR image intensity valuesacross different MR scanner types (e.g., manufactured by GE, Siemens andthe like; or various magnetic field strengths such as 0.5 Tesla, 1.5Tesla, etc.). Pre-processing techniques can also be used for removing orreducing image artifacts for new patent MR scan 310 (shown in FIG. 3).If image artifact reduction is not conducted, process 600 continues tostep 650 to extract features. In some embodiments, one or more steps ofthe pre-processing (e.g., as enclosed by the dash lines in FIG. 6) maybe omitted.

At step 650, features can be extracted from the training data 110. Insome embodiments, system 500 may extract features from every voxel ofeach MR scan in the training data 110. For example, the MR image itselfcan be used and each voxel or selected voxels from the MR image can beused to extract features. Alternatively, processor 514 may segment theMR image into different tissue types and segment the image voxels ofeach MR scan based on tissue types. This can be advantageous in somecases because the tissue types can be used as an additional feature, forexample, in addition to other extracted features.

At step 660, system 500 may create an MR feature vector based on theextracted image features for each voxel of the MR scan. Therefore, avector containing a plurality of features for each voxel of the MR scancan be produced by processor 514. A plurality of MR feature vectors 120can be produced by processor 514 for a plurality of voxels of the MRscan.

At step 670, system 500 may extract a CT value from each voxel of eachCT scan in the training data.

At step 680, system 500 may determine whether to reduce the number ofdimensions associated with the MR feature vectors 120.

For example, processor 514 of system 500 may determine that the numberof dimensions associated with MR feature vectors 120 would lead to ahigh computational cost or potentially cause performance problems whenprocessed by the pseudo-CT predictive model 150. In another example,system 500 may determine that MR feature vectors 120 include noise orduplicated data exceeding thresholds considered to affect the accuracyof pseudo-CT predictive model 150. In another embodiment, system 500 maydetermine whether to conduct dimensionality reduction based on factorsaffecting performance and/or output quality. Therefore, if processor 514determines that dimensionality reduction is required, process 600follows branch 682 (e.g., “YES”) to step 686, in which processor 514 canreduce the dimensions associated with MR feature vectors 120.Alternatively, in some embodiments, system 500 may receive input (e.g.,from a user) to not perform any dimensionality reduction of the MRfeature vectors 120.

If no dimensionality reduction is required, process 600 may proceeddirectly along branch 684 to step 690. At step 690, system 500 mayutilize statistical or machine learning techniques to generate apseudo-CT prediction model 150 based on the MR feature vectors (e.g.,120) and extracted CT values. In some embodiments, the reduced-dimensionfeature vectors 140 may be utilized.

According to some embodiments, a subset of training data 110 may be usedas a basis to train and build the pseudo-CT prediction model 150. Thus,system 500 may determine (e.g., based on user input and/or systemsettings reflected in treatment planning software 511 and/ortraining/prediction software 513) a subset of training data 110 to trainand build the pseudo-CT prediction model 150. In another embodiment, thesubset of training data 110 can be classified based on particular imageregions. For example, the subset of training data 110 can be: 1) withrespect to particular anatomical regions, 2) with respect to varioustissue classifications, or 3) with respect to training subjectcharacteristics.

For example, one or more features may provide superior interpretation ofthe underlying anatomical structure for a given patient MR scan; andthus, only the subset of superior features may be extracted fromtraining data 110 for use in training pseudo-CT prediction model 150.Using the superior features, the predictive power of pseudo-CTprediction model 150 to estimate corresponding pseudo-CT values 340 forthe given patient MR scan may be improved. The subset of features can beused to generate and train one or more pseudo-CT models.

In an embodiment, when building a pseudo-CT prediction model withrespect to a particular anatomical region, a subset of training data 110(e.g., only training data 110 associated with the body region ofinterest) may be used to train and build the pseudo-CT prediction model150. Instead of one pseudo-CT prediction model, processor 514 cangenerate a plurality of pseudo-CT prediction models with respect toparticular anatomical areas of the body (e.g., head, upper body, lowerbody, and the like). Thus, processor 514 can utilize the MR featurevectors 120 (or reduced dimension feature vectors 140) and the CT values130 for a predetermined anatomical location of interest to generate apseudo-CT prediction model 150 for that particular anatomical locationof interest depicted in the MR scan.

For example, system 500 may determine that the patient MR scan 310includes an MR image of the patient's prostate. Thus, consistent withdisclosed embodiments, system 500 may identify a pseudo-CT predictionmodel that has been built and trained based on training data 110utilizing one or more MR scans and CT scans of a prostate as trainingdata. In an embodiment, more than one pseudo-CT prediction model may beavailable, where each model may depict various anatomical aspects of aprostate, for example. Therefore, a plurality of pseudo-CT predictionmodels may be generated, where each pseudo-CT prediction model is for aparticular anatomical area (e.g., a pseudo-CT prediction model for aprostate, a pseudo-CT prediction model for a right lung, a pseudo-CTprediction model for a left lung, a pseudo-CT prediction model for abrain, and the like).

In another embodiment, the pseudo-CT predictive model 150 may begenerated based on classification-based features, such as a tissueclassification. For instance, system 500 may use the image featureextraction module 111 to segment the image voxels of each MR scan in thetraining data 110, according to a tissue class (e.g., bone, fat, muscle,water, air, and structural classes such as cardiac tissue, lung tissue,liver tissue, brain tissue, and the like). A plurality of segmentationmaps for each MR scan can be generated based on the segmented imagevoxels. The image features can be extracted from the segmentation maps.The segmentation map extracted image features may be combined with theMR scan extracted image features for each voxel. The MR feature vectorsmay be determined for each training subject based on the combined imagefeatures. A pseudo-CT prediction model based on the combined MR featurevectors and extracted CT values can be generated. As stated above, theterm “tissue” is being used as a classification and not merely tosuggest specific types of tissue (e.g., air is not a tissue).

In a still further embodiment, a process for generating pseudo-CTimage(s) can be based on using training data selected according totraining subject characteristics. For example, system 500 may identifyone or more common characteristics among a subset of the trainingsubjects. For example, system 500 may identify the age, gender, weightclass, etc., associated with each training subject and select trainingsubjects having one or more common characteristics. In other examples,system 500 may identify one or more characteristics of the trainingsubjects based on the MR and CT scan(s) in the training data 110.Further, system 500 may identify one or more characteristics of thepatient (e.g., a new patient) in common with a subset of the trainingsubjects. For example, system 500 may identify one or morecharacteristics of the patient and compare the patient's characteristicswith those identified for the training subjects to identify commoncharacteristics. System 500 may then select training subjects having theone or more common characteristics as training data to train and buildpseudo-CT prediction model 150.

Image features may be extracted from the MR scans and CT numbers fromthe CT scans associated with the characteristics of training subjects.For example, system 500 may extract image features from MR scans and CTvalues 130 from CT scans in training data 110 associated with a subsetof the training subjects having common characteristics with the newpatient. Then, MR feature vectors can be determined for each trainingsubject of the subset based on the extracted image features. Pseudo-CTprediction model can be generated based on these MR feature vectors andextracted CT values.

The pseudo-CT prediction model 150 can be trained using all trainingdata 110 and then utilized for a new MR scan for a new patient. Thepseudo-CT prediction model 150 can also be used for all future newpatients. In some embodiments, the same pseudo-CT prediction model 150may not be used for every patient. A pseudo-CT prediction model 150 maybe custom generated for a particular patient. For example, the trainingdata may be selected based on training subjects that are similar orrelevant to the new patient and a model can be built specifically forthe new patient.

Medical personnel can find it useful to assess both the MRcharacteristics and the CT characteristics of a region of interest in apatient to determine an optimal treatment or diagnosis. Further, thepseudo-CT model can be used to derive a CT image from an MR image tofacilitate patient dose computation in radiation therapy treatmentplanning. This is desirable for accurately generating a pseudo-CT imagefrom an MR image in order for patients to be spared from additionalradiation exposure arising from CT imaging. In order to replace a realCT image, the pseudo-CT image should be as close as possible to a realCT image of the patient for the purpose of dose computation in radiationtherapy treatment planning or for generating digitally reconstructedradiographs (DRRs) for image guidance. However, there is not a simplemathematical relationship between CT image intensity values (CT values)and MR intensity values. The difficulty arises because MR intensityvalues are not standardized and can vary significantly depending upondifferent MR scanner settings or different MR imaging sequenceparameters.

FIG. 7 is a flowchart of an exemplary process 700 for using thepseudo-CT predictive model (as described by FIG. 1 and FIG. 6) after themodel has been trained to generate pseudo-CT values and pseudo-CT images(as described in FIG. 3), consistent with the disclosed embodiments. Atstep 710, system 500 may receive at least one MR scan (e.g., MR scan310) associated with a patient (e.g., a new patient). The at least oneMR scan may not have a corresponding CT scan. The MR scan is used togenerate a pseudo-CT image.

At step 720, processor 514 may determine whether the MR image voxelsshould be segmented. Segmenting the MR scan is optional. Processor 514can receive instructions from either the treatment planning software 511(shown in FIG. 5) or from a user interface (not shown) to indicatewhether the MR scan should be segmented. If so, process 700 followsbranch 722 (e.g., “YES”) to segment the MR scan. At step 730, the imagevoxels of the MR scan are segmented according to, for example, tissueclassification. Segmentation can be performed according to segmentationtechniques known to those of skill in the art. For example, processor514 may employ a k-means clustering segmentation method, a fuzzy C-meanssegmentation method, and the like to create one or more segmentationmaps.

Processor 514 may further use more advanced segmentation methods. Forexample, processor 514 may employ a learning-based or feature-basedapproach to perform segmentation, which may include building aclassification prediction model using, for example, algorithms (e.g.,local pattern feature, landmark feature, context feature, and the like)to predict a tissue label for each image voxel based on features of theimage voxel.

At step 732, processor 514 may generate a plurality of segmentation mapsfor each MR scan based on the segmented image voxels to create aclassification prediction model. For example, a binary bone segmentationmap may be an image with values equal to “1” at voxels labeled as bonesand “0” at all other voxels. The processor 514 may use the segmentationmaps to extract additional features from the original MR images.Consistent with disclosed embodiments, learning-based methods disclosedabove may be employed to train and build a predictive model forgenerating the one or more segmentation maps. Alternatively, ifsegmentation of the MR scan is not needed, process 700 follows branch724 (e.g., “NO”).

At step 740, processor 514 may extract image features from the patient'sMR scan. If the optional path of segmenting the MR scan, describedabove, was performed, then the extracted image features can be providedalong path 722. At step 734, processor 514 may combine the additionalfeatures extracted by using the segmentation map from the MR scan alongwith the features extracted directly from the MR images to form acombined set of features for each data point (e.g., voxel).

Regardless of whether the MR scan is segmented or not, after the imagefeatures have been extracted, process 700 proceeds (e.g., along path 744or 736) to step 750. At step 750, processor 514 may determine featurevectors 120 for each training subject from the extracted image features.

At step 760, processor 514 may input the MR feature vectors 120 intopseudo-CT prediction model 150. At step 770, processor 514 may apply thepseudo-CT prediction model 150 to the input MR feature vectors 120 todetermine the CT numbers (e.g., pseudo-CT values 340) for each voxel ofthe patient MR image 310.

At step 780, based on the pseudo-CT values 340, processor 514 maygenerate a pseudo-CT image 350 for the patient. The resulting pseudo-CTimage 350 may be used for the purposes of dose computation in treatmentplanning, generating DRRs for image guidance, and the like.

FIG. 8 illustrates a diagram of an exemplary method for augmentingtraining data to build a pseudo-CT predictive model, consistent with thedisclosed embodiments. The method shown in FIG. 8 may also be referredto as a cascade training technique or a multi-stage training technique,in which an initially trained predictive model is used to produce anintermediate prediction result, which in turn is used as part of thetraining data to further refine the predicative model. The cascadetraining technique may include multiple training stages. In each stage apredictive model is trained using initial data (e.g., training data 110)combined with predication result produced by the predictive modelgenerated in a prior stage.

In one embodiment, a pseudo-CT predictive model 150 may be initiallybuilt and trained using an initial set of training data, as describedabove with respect to FIG. 1. As part of the initial training process,image features are extracted from the plurality of image scans andfeature vectors are determined from the extracted image features. Inaddition, the corresponding CT values can be determined from the CTscans. For example, at least one CT image for each training subjectshould be available. In an embodiment, multiple CT images may beavailable. If multiple CT image are available, the CT images can beaveraged to reduce image noise. A pseudo-CT model may be trained in anystage of a cascade training process.

In another embodiment, a classification prediction model may be trainedas an initial model or any intermediate model. As discussed above, theclassification prediction model may be used to predict one or moresegmentation maps, from which classification-based features may beextracted and used in the next training stage.

In an exemplary cascade training process, pseudo-CT predictive modelsand classification prediction models may be used in any combinationamong the multiple stages, so long as a pseudo-CT predictive model istrained and built in the last stage.

As shown in FIG. 8, the initially built predictive model is shown asModel #1. Model #1 is generated by processing the original trainingdata, such as each MR scan, each CT scan, or each pair of MR and CTscans (e.g., 110 a, 110 b) during Training Stage #1. As discussed above,Model #1 may be a pseudo-CT predictive model or a classificationprediction model. Model #1 can then be used in the training process ofthe next stage. For example, Model #1 can be used to generate aplurality of prediction results (e.g., prediction result 1, stage 1;prediction result 2, stage 1, . . . prediction result N, stage 1). Forexample, when Model #1 is a pseudo-CT predictive model, the MR scan oftraining subject a can be used as input (e.g., as if training subject ais a new patient and the MR scan of training subject a is a new MR scan)to Model #1 to generate a pseudo-CT prediction result 1120 a: predictionresult 1, stage 1. In another example, when Model #1 is a classificationprediction model, the prediction result of Model #1 may a segmentationmap. Other prediction results 1120 b . . . 1120N can be similarlygenerated. These prediction results can then be augmented to the initialtraining data to form augmented training data 1120. For example, byassociating the pairs of MR and CT scans (e.g., 110 a, 110 b, . . .110N) with their corresponding prediction results (e.g., 1120 a, 1120 b,. . . 1120N) generated from Model #1, augmented training data 1120 canbe created. The augmented training data 1120 can be used by a trainingmodule (e.g., training module 412) in the next training stage togenerate another model (e.g., Model #2).

In this way, a new, refined, trained model can be generated (e.g., Model#2, Model #3, . . . Model #M) at each stage. The model developed at aprevious stage can be refined by applying the augmented training data(e.g., augmented training data 1120, 1130, etc.) to the training module.For example, Model #2 can be generated using the augmented training data1120 including the prediction results generated by Model #1. In order togenerate Model #2, the augmented training data 1120 can be input intothe training module (e.g., training module 412) in training stage #2. Insome embodiments, the prediction result generated by Model #1 may be animage (e.g., a pseudo-CT image) or a map (e.g., a segmentation map) foreach training subject. Image features may then be extracted from theprediction result (e.g., image or map) using, for example, image featureextraction module 111. The image features that can be extracted from theprediction result may include any features discussed in prior passages,such as intensity features, context features, patch features, localpatter features, landmark features, etc. The features extracted from theprediction result may be combined with the features extracted from theoriginal MR images to form a new, extended feature vector for each imagepoint. The extended feature vector may be used in the next trainingstage to train, for example, Model #2. As each subsequent predictionmodel (e.g., Model #2) is built and trained using prediction results ofits prior prediction model (e.g., Model #1), new information revealedfrom the prediction results can be added into the training process andthe performance of the subsequent prediction model can be improved. Thisprocess of using augmented training data (e.g., augmented training data1120, 1130, etc.) to train each successive model (e.g., Model #1, Model#2, . . . etc.) continues until a final prediction model Model #M istrained and built. If the goal of the cascade training process is tobuild a pseudo-CT prediction model, then the last model, Model #M is apseudo-CT prediction model, while all other models in the intermediatestages can be any models. The number of stages utilized can depend uponthe validation of the Model #M to accurately predict the CT values. Forexample, this iteration process can stop when the difference betweenpseudo-CT prediction values generated by the latest model and theoriginal CT values is less than a predetermined threshold. In anotherexample, the iteration process can stop when the difference betweenprediction results of successive pseudo-CT prediction models is lessthan a predetermined threshold.

As described above, the cascade training technique is applicable totraining and building pseudo-CT prediction models and/or tissueclassification models. When training and building tissue classificationmodels, each intermediate predictive model (e.g., Model #1, Model #2, .. . Model #M−1) can be a tissue classification model that can, forexample, provide segmentation maps reflecting tissue labels instead ofproducing pseudo-CT values. Using the multi-stage training processdiscussed above, each tissue classification model can be built andtrained using the augmented data including the prior model's predictionresults to continually refine the model at each stage. In addition,tissue classification and pseudo-CT prediction can be mixed into themulti-stage process. For example, in the first K stages, the modeltrained may be a tissue classification model and the prediction resultsmay be tissue classification results. Once the tissue classificationmodel is trained, the tissue classification result may then be used inthe K+1 stage to predict the pseudo-CT values, where the tissueclassification result can be used to extract a set of features togetherwith other features extracted from the MR scans. A single extra stagemay be performed (e.g., if M=K+1) or additional stages may be performeduntil M stages are reached (e.g., if M>K+1). At the end of the process,the final prediction model, Model #M is trained and built to generatepseudo-CT values.

FIG. 9 illustrates a diagram of an exemplary process for applyingmulti-stage models to predict pseudo-CT values, consistent with thedisclosed embodiments.

As shown in FIG. 9, a processor (e.g., processor 514 shown in FIG. 5)may acquire one or more patients' MR scan(s) 310 from an imageacquisition device 440 or from databases 520, 530 (shown in FIG. 5).

Once the patient's MR scan is acquired, a plurality of image featuresmay be extracted for each MR voxel of the MR scan(s). As discussed abovewith respect to FIG. 2, the extracted image features may include a localpattern feature 212, a landmark feature 214, a context feature 216, andvarious other types of features 218. The features can be associated withan image point, an image voxel, or an image sample. As shown in FIG. 9,image feature extraction module 311 may be used to extract imagefeatures from the MR scan(s). Image features may refer to numerical(e.g., intensity values, coordinate locations of the feature and thelike) or categorical properties (e.g., a tissue type, a structure labeland the like) of an MR voxel. The extracted image features for eachimage point may form a feature vector, as discussed above. A set offeature vectors (e.g., 320), for example for all the image points of theMR scan(s) 310 may be input into Model #1 (the model generated intraining stage 1 in FIG. 8). In other words, processor 514 may applyModel #1 generated in training stage 1 in the multi-stage trainingprocess of FIG. 8 to the set of feature vectors extracted from MR scan310. Model #1 may output prediction results #1 940 (e.g., pseudo-CTvalues, segmentation maps, etc., depending on the type of Model #1).Subsequently, prediction results #1 940 and the MR scan(s) 310 may becombined and subjected to another image feature extraction 311. Becausemore information is provided by prediction results #1 940, more imagefeatures may result from the second extraction or the image featuresresult from the second extraction may have better quality than thoseresult from the first extraction. The image features result from thesecond extraction may form a set of MR feature vectors that can be inputinto Model #2 generated in training stage 2 of FIG. 8 to generateprediction results #2 950, which may be combined with MR scan(s) 310.Image feature extraction module 311 may again extract a set of MRfeature vectors from the combined prediction results #2 950 and MRscan(s) 310. This process is repeated, until the final prediction modelModel #M is applied to generate pseudo-CT values 340. According to someembodiments, prediction models Model #1 through Model #M should beapplied in the same order as that of the models generated in thetraining process. The pseudo-CT values 340 may be used to generatepseudo-CT image 350 depicting an accurate visual representation of thepatient's anatomical geometry.

In some embodiments, any one of the predictive models Model #1, Model#2, . . . Model #M−1 may be a tissue classification model. For example,Model #1 may be a classification model. Applying Model #1 to featurevectors extracted from MR scan(s) 310 can generate a classification map,such as a tissue classification map. The tissue classification map,along with the MR scan(s) 310 may be used as input to image featureextraction module 311 to generate feature vectors having moreinformation or improved quality, to which Model #2 can be applied.Similar steps can be repeated to further refine the feature vectors. Aseries of classification models may be provided from the multi-stagetraining process (FIG. 8). For example, Model #1 may be a classificationmodel A, Model #2 may be a classification model B, and the like.Classification models A and B may be associated with the same tissueclass but different refinements, or may be associated with differenttissue classes. As discussed above, the final prediction Model #M is apseudo-CT predictive model that produces pseudo-CT values 340,consistent with the disclosed embodiments.

In some embodiments, the predictive model can be built and trained withtraining data 110 (shown in FIG. 1) that includes multi-channel MR scansand corresponding CT values. Multi-channel MR scans provide moreinformation than single-channel MR scans. The increased informationavailable with multi-channel MR images allows for more accurate and morerobust prediction of CT values to generate a pseudo-CT image. Forexample, multi-channel MR images allow for conversion from MR intensityvalues to intrinsic tissue parameter values.

MRI is a highly versatile imaging technique that permits the study ofvarious properties (e.g., both structural and functional) of the humanbody through the manipulation of magnetic and radio frequency (RF)fields. For standard structural (or anatomical) imaging, the measured MRsignal (e.g., MR image intensity) can be a function of a few intrinsictissue parameters: the proton density (P), the longitudinal relaxationtime (T₁), and the transverse relaxation time (T₂, or T₂* if consideringa magnetic field inhomogeneity effect). For example, for both FLASH andSPGR imaging protocols (e.g., also known as imaging sequences), the MRsignal intensity S can be expressed as a function of intrinsic tissueparameters (P, T₁, and T₂*) according to Equation 2:

$\begin{matrix}{{S = {P\; \sin \; {\alpha\left( \frac{1 - e^{{{- {TR}}/T}\; 1}}{1 - {\cos \; \alpha \; e^{{{- {TR}}/T}\; 1}}} \right)}e^{{- {TE}}/T_{2}^{*}}}},} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$

where TR, TE, and α are the MR acquisition parameters that the user isfree to modify. Different parameters can be used to produce differentimage contrasts.

In order to predict CT numbers from MR images, a predictive model mayrely primarily on the intrinsic tissue parameters (P, T₁, and T₂*),instead of the MR signal intensity S that is dependent on sequenceparameters (TR, TE, and α), because the latter does not, at leastdirectly, represent anatomical properties of the patient.

The use of multi-channel MR images allows the estimation of theseintrinsic tissue parameters, because multi-channel MR images can providea plurality of images with each image having a different setting of thesequence parameters (TR, TE, and α). Therefore, multi-channel MR imagesallow for the estimation of intrinsic tissue parameters (P, T₁, and T₂*)by solving Equation 2 in which multiple values of S and sequenceparameters (TR, TE, and α) are known. For example, to estimate all threeunknown parameters (P, T₁, and T₂*), three MR images (e.g., threechannels) are needed. Using additional channels would improve therobustness of the parameter estimation by reducing image noise.

In some embodiments, the workflow for implementing the disclosed methodincludes two stages: training (e.g., model construction) stage andapplication (e.g., pseudo-CT generation) stage. In some embodiments, thetraining stage only needs to be computed once after the training dataare collected. After the prediction model is trained, in the applicationstage, the trained model can be applied to new multi-channel MR scans tocreate a pseudo-CT image for a new patient with only multi-channel MRscans. In the following description, FIGS. 10 and 11 are directed to thetraining stage, while FIG. 12 is directed to the prediction stage.

FIG. 10 illustrates a diagram of an exemplary process for training apseudo-CT predictive model using tissue parameters estimated frommulti-channel MR scans. Similar to the process shown in FIG. 1, trainingdata from a plurality of training subjects having both CT scans andmulti-channel MR scans can be collected. For example, as shown in FIG.10, multi-channel MR image data 1010 a and CT image data 1014 a can becollected for subject a. Multi-channel MR data 1010 a may include aplurality of MR images obtained using different sequence parameter sets.Similarly, multi-channel MR image data 1010 b and CT image data 1014 bcan be acquired for subject b. The multi-channel MR image data and CTimage data can be collected from image acquisition device (e.g., 440) orfrom image database (e.g., 520, 530). The process of dataacquisition/collection goes on until the nth set of data (e.g., 1010 nand 1014 n) has been included in the training data. In one embodiment,the training data shown in FIG. 10 are similar to training data 110 inFIG. 1 except that the MR image data in FIG. 10 are multi-channel MRdata while the MR data in FIG. 1 can be either single-channel ormulti-channel MR data.

In some embodiments, the CT scans (e.g., 1014 a) and multi-channel MRscans (e.g., 1010 a) are aligned. If not, auto- or semi-automated imageregistration or alignment procedure can be applied to align them. Asdescribed above, an aligned pair of CT image and MR image means that foreach voxel (e.g., indicating a spatial location of the image) thecorresponding CT and MR image values are known or the correspondencerelationship is known. In addition, MR images may also undergo someprocedure for correcting geometric distortions.

The present application discloses a learning-based approach to build andtrain a prediction model using training data. In an embodiment, theprediction model can be a regression model or a regression function asthe output of the prediction model can be a continuous variable (e.g., aCT value).

Many statistical or machine learning methods can be used to build and/ortrain a prediction model that can predict the CT image intensities (alsoknown as CT values or CT numbers) based on features derived from the MRimages. For example, supervised learning is a branch of machine learningthat can be used to determine a prediction model based on a set oftraining data. Each sample of the training data is a pair includinginput data (e.g., a vector of measurements or features) and a desiredoutput value (e.g., a supervisory signal). A supervised learningalgorithm can analyze the training data and produce a predictor function(e.g., a regression function) when the output variable is numeric orcontinuous, which is usually true in the application of generatingpseudo-CT images. Various algorithms can be applied to determine theprediction model, including but not limited to: support vector machines,neural networks, decision trees, and random forests.

The predictive model, once trained using the training data, can be usedto generate pseudo-CT images for any new set of multi-channel MR scansof the same or a different patient.

Embodiments of the present application may convert MR intensity values(e.g., S) to intrinsic tissue parameters (e.g., P, T₁, T₂*, or T₂*) andconstruct the prediction model based on intrinsic tissue parameters. Asdiscussed above, the ability to use MR imaging provides greaterflexibility and less radiation exposure over CT imaging but the MRintensity values cannot be directly used in dosage calculation becausethey are sequence dependent. Training the predictive model using theintrinsic tissue parameters instead of using raw MR intensity values mayprovide a predictive model that is sequence independent. Being sequenceindependent can be advantageous because the imaging sequences orsequence parameters are easily modifiable and often vary significantlyamong different clinics. By designing the predictive model to besequence independent, data acquired from different MR scanners,different MR imaging sequences, or different clinics can be usedtogether, provided that the MR sequences can be used to estimateintrinsic tissue parameters. In addition, the MR imaging sequences fornew patients do not need to be the same as the MR imaging sequences usedby the training data. Therefore, a user can freely design new MR imagingsequences for future patients without the need to acquire new trainingdata to train the predictive model.

To build a prediction model based on tissue parameters, the MR imageintensities need to be converted into tissue parameter values for eachpatient. This can be achieved by solving an MR imaging equation (e.g.,Equation 2) at every image point (e.g., voxel) of the patient's MRimages. A set of tissue parameter images (also referred to as tissueparameter maps) can be produced. The set may include one tissueparameter map for each of the tissue parameters. For example, the setmay include a map of P, a map of T₁, and a map of T₂ or T₂*. The tissueparameter values are intrinsic values reflecting the properties of theunderlying tissue or organ of the patient's body. Further, because theCT image is aligned with the MR images for each training subject, the CTimage is further aligned with the tissue parameter maps generated fromthe MR images.

As shown in FIG. 10, tissue parameter maps for each training subject maybe generated based on multi-channel MR image data. For example, tissueparameter maps 1012 a of subject a may include a set of all three tissueparameter maps. In some embodiments, if estimation of all tissueparameters from multi-channel MR images is difficult, a model can alsobe built using only a subset of the tissue parameters. Once tissueparameter maps of each training subject are obtained (e.g., 1012 b, . .. , 1012 n), tissue parameter maps may be used together with thecorresponding CT image to build and train a pseudo-CT prediction model1050 using a training module 1045 (e.g., using statistical or machinelearning techniques). In some embodiments, for each image point (e.g.,voxel), the set of tissue parameters may be treated as features includedin the feature vector as described in FIG. 1. For example, if all threetissue parameters are used, a feature vector of [P, T₁, T₂*] may beconstructed. This tissue parameter feature vector may be used alone orin combination with other features as discussed in connection with FIG.2 for building and training a predictive model. The disclosed techniquesof building and training predictive model 150 discussed above are alsoapplicable to the process of building and training predictive model1050.

FIG. 11 is a flow chart of an exemplary method 1100 of building apseudo-CT predictive model using multi-channel MR data. Method 1100 maybe implemented by system 500. At step 1110, processor 514 may receivetraining data including multi-channel MR data (e.g., 1010 a, 1010 b,etc.) and CT data (e.g., 1014 a, 1014 b, etc.). The multi-channel MRdata may include multi-channel MR images for one or more patients. Insome embodiments, the multi-channel MR data may include at least twomulti-channel MR images for each patient. Multi-channel MR images of thesame patient may be obtained using different imaging sequenceparameters. The CT data may include CT images for one or more patients.In some embodiments, the CT data may include at least one CT image foreach patient. If multiple CT images are available, they can be averagedto reduce image noise. For a given patient, the CT image and themulti-channel MR images may be aligned. In some embodiments, if the CTimage and the multi-channel MR images are not aligned, imageregistration techniques may be used to align them.

At step 1120, processor 514 may determine at least one tissue parametermap (e.g., 1012 a, 1012 b, etc.) based on MR intensities from themulti-channel MR data. For example, at least one tissue parameter map ofP, T₁, or T₂* may be estimated using MR intensity values of multiple MRimages of a patient by solving Equation 2. Because Equation 2 is anonlinear equation, fitting techniques can be used to estimate tissueparameter values based on multiple sets of MR intensities (S) andsequence parameters (TR, TE, and α), which are known based on themulti-channel MR data. As described above, all three tissue parametermaps are preferred, but a subset of the tissue parameter maps may alsobe used.

In some embodiments, a tissue parameter map may be generated byestimating individual image points. For example, a set of tissueparameter values including several kinds of tissue parameters (e.g., P,T₁, T₂* or T₂*) may be estimated at every image point. The correspondingtissue parameter map can then be formed as a collection of all tissueparameter values of a particular kind.

At step 1130, processor 514 may obtain CT values corresponding to thetissue parameter map(s) generated at step 1120. In some embodiments, theCT values may be the CT intensity values of the CT image for the samepatient as the tissue parameter map(s). As described above, because theCT image is aligned with the MR images, the CT image is also alignedwith the tissue parameter map(s) converted from the MR images.

At step 1140, processor 514 may generate a pseudo-CT prediction model(e.g., 1050) based on the CT values and the tissue parameter map(s). Insome embodiments, the CT values and the tissue parameter map(s) may beinput to a training module (e.g., 1045) to train the pseudo-CT model1050. Regression methods such as statistical learning or machinelearning techniques may be used by training module 1045 to train thepredictive model. The trained predictive model 1050 may be amathematical or statistical model that can be used to predict a CTnumber based on one or more tissue parameter values (e.g., P, T₁, T₂*,T₂*). As described above, while it is preferable to use all tissueparameters, a model can also be built using only a subset of the tissueparameters.

FIG. 12 is a flowchart of an example method 1200 for generating apseudo-CT image for a patient using multi-channel MR images. Method 1200may be implemented by system 500 and used to generate pseudo-CT imagesfor a new patient using the predictive model (e.g., 1050) built bymethod 1100.

At step 1210, processor 514 may receive multi-channel MR images of apatient (e.g., a new patient) from, for example, image acquisitiondevice 440 or database 520, 530. The received multi-channel MR imagesmay not have corresponding CT images. At step 1220, processor 514 mayconvert the multi-channel MR images into at least one tissue parametermap. For example, processor 514 may convert the multi-channel MR imagesinto tissue parameter map(s) by solving Equation 2 using fittingtechnique or the like. One or more tissue parameter maps, such as mapsof P, T₁, T₂*, and/or T₂*, may be generated by the conversion process1220. As described above, while it is preferable to have all tissueparameter maps, in some embodiments a subset of the maps may also beused.

At step 1230, processor 514 may apply a predictive model (e.g., 1050) tothe tissue parameter map(s). For example, the converted tissue parametermap(s) may be used as input to the predictive model. In someembodiments, the predictive model may operate in a point-wise manner, inwhich the predictive model predicts the CT number at each location ofthe patient image (e.g., pseudo-CT image) based on tissue parametervalue(s) computed at that location. More complex prediction models mayalso be used. For example, a model can take into account of nearbytissue parameter value(s) for each point, which can improve robustnesswith respect to data noise. A model may also make prediction based on acombination of the tissue parameter value(s) and other information suchas the image point location or other features that can be derived fromthe tissue parameter map(s)—e.g., texture, gradient, etc.

At step 1240, a pseudo-CT image for the patient may be generated byassembling the pseudo-CT values resulting from step 1230.

To train a predictive model and to use the model to predict thepseudo-CT values, parametric methods such as linear regression orordinary least squares regression may be used. In these parametricmethods, the regression function is defined in terms of a finite numberof unknown model parameters that can be estimated from input data. Forexample, a regression model can be defined as:

H≈f(X,β),

where H denotes the CT values to be predicted, X denotes a vector ofinput variables, e.g., tissue parameter values (P, T₁, and T₂* (or T₂)),and β denotes a vector of unknown model parameters for the regressionmodel. One exemplary model is a linear regression model defined as:

H≈β ₁ P+β ₂ T ₁+β₃ T ₂*

In the training stage, training data can provide a large number ofobservations, for example, a set of known H values (e.g., provided by CTscans at step 1130) with corresponding P, T₁, and T₂* values (e.g.,provided by converting MR scans at step 1120). Using these observationdata, model parameters β can then be computed or trained (e.g., usingleast square fitting). Once β is obtained after training, the model canthen be used to compute H for a new set of P, T₁, and T₂* values (e.g.,in the prediction stage using method 1200).

In some embodiments, instead of or in addition to using the tissueparameters as the input data for the prediction model, other informationcan also be collected. For example, extra features that can be computedor collected include but not limited to:

-   -   The coordinates of an image point, or normalized coordinates        with respect to an external reference space or to one or a few        internal landmark points;    -   Curvatures of the tissue parameter map(s) computed at a sample        point location;    -   Texture measures of the tissue parameter map(s) computed at a        sample point location; and    -   Patches of local tissue parameter values, e.g., tissue parameter        values within a 5×5×5 neighborhood of a sample point.

Computer programs based on the written description and methods of thisspecification are within the skill of a software developer. The variousprograms or program modules can be created using a variety of softwareprogramming techniques. For example, program sections or program modulescan be designed in or by means of Java, Python, C, C++, assemblylanguage, or any known programming languages. One or more of suchsoftware sections or modules can be integrated into a computer systemand/or computer-readable media.

Moreover, while illustrative embodiments have been described herein, thescope includes any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations or alterations based on the presentdisclosure. The elements in the claims are to be interpreted broadlybased on the language employed in the claims and not limited to examplesdescribed in the present specification or during the prosecution of theapplication, which examples are to be construed as non-exclusive. It isintended, therefore, that the specification and examples be consideredas example only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1. (canceled)
 2. A computer-implemented method for training a machinelearning model to generate a pseudo-CT image, the method comprising:accessing, by a processor, training data comprising a ComputerizedTomography (CT) image and a Magnetic Resonance (MR) image; extracting afeature from the MR image; extracting a CT value, corresponding to theextracted feature of the MR image; estimating a parameter thatrepresents a relationship between the extracted feature of the MR imageand the extracted CT value corresponding to the extracted feature of theMR image; and training the machine learning model to generate apseudo-CT image for another MR image based on the estimated parameter.3. The method of claim 2, wherein the training data include multiple CTimages for at least one of the plurality of training subjects, furthercomprising averaging CT values of corresponding image points of themultiple CT images.
 4. The method of claim 2, further comprisingaligning the MR image and the CT image.
 5. The method of claim 2,wherein the training data include a first set of MR images for a firsttraining subject and a second set of MR images for a second trainingsubject, and the first and second sets of MR images have differentsequence parameters, further comprising training the machine learningmodel using data from both the first and second sets of MR images. 6.The method of claim 2, wherein the feature extracted from the MRincludes at least one of: coordinates of an image point; normalizedcoordinates with respect to a reference point external to the MR image;normalized coordinates with respect to one or more landmark pointsinside the MR image; one or more curvatures in the at least one tissueparameter map; one or more texture measures of the at least one tissueparameter map; or one or more image patches in the at least one tissueparameter map.
 7. The method of claim 2, wherein the training datacomprises multiple MR images respectively associated with different MRimaging protocols.
 8. The method of claim 7 further comprising:obtaining one or more MR imaging protocol parameters associated with theMR images; determining one or more intrinsic tissue parameters based onthe one or more MR imaging protocol parameters; generating one or moretissue parameter maps for the one or more intrinsic tissue parameters;and calculating, from the CT image, one or more CT values correspondingto the one or more tissue parameter maps, wherein the machine learningmodel is trained based on the calculated one or more CT values.
 9. Acomputer-implemented method for generating a pseudo-CT image, the methodcomprising: receiving an Magnetic Resonance (MR) image of a patient;generating a Computerized Tomography (CT) value by applying a machinelearning model to a feature of the MR image, wherein the machinelearning model is trained to generate a pseudo-CT image for a receivedMR image based on an estimated parameter that represents a relationshipbetween an extracted feature of a training MR image and an extracted CTvalue corresponding to the extracted feature of the training MR image;and generating the pseudo-CT image based on the generated CT value. 10.The method of claim 9, wherein the received MR image is a first MRimage, further comprising receiving a second MR image, wherein the firstand second MR images are respectively associated with different MRimaging protocols.
 11. The method of claim 10 further comprising:obtaining one or more MR imaging protocol parameters associated with theMR images; determining one or more intrinsic tissue parameters based onthe one or more MR imaging protocol parameters; generating one or moretissue parameter maps for the one or more intrinsic tissue parameters;and generating one or more CT values by applying the machine learningmodel to the one or more tissue parameter maps.
 12. The method of claim9, wherein the model is trained by a first set of multi-channel MRimages having a first set of MR imaging protocol parameters and a secondset of multi-channel MR images having a second set of MR imagingprotocol parameters that is different from the first set of MR imagingprotocol parameters.
 13. The system of claim 12, wherein the first andsecond set of MR imaging protocol parameters are associated with adifferent respective MR acquisition setting.
 14. A system for generatinga pseudo-CT image, comprising: a processor; a memory communicativelycoupled to the processor, the memory storing instructions that, whenexecuted by the processor, configure the processor to perform operationscomprising: storing training data comprising a Computerized Tomography(CT) image and a Magnetic Resonance (MR) image; extracting a featurefrom the MR image; extracting a CT value corresponding to the extractedfeature from the MR image; estimating a parameter that represents arelationship between the extracted feature of the MR image and theextracted CT value corresponding to the extracted feature of the MRimage; and training the machine learning model to generate a pseudo-CTimage for a subsequently received MR image based on the estimatedparameter.
 15. The system of claim 14, wherein the training data includemultiple CT images for at least one of the plurality of trainingsubjects, further comprising operations for averaging CT values ofcorresponding image points of the multiple CT images.
 16. The system ofclaim 14 further comprising operations for aligning the MR image and theCT image.
 17. The system of claim 14, wherein the training data includea first set of MR images for a first training subject and a second setof MR images for a second training subject, and the first and secondsets of MR images have different sequence parameters, further comprisingoperations for training the machine learning model using data from boththe first and second sets of MR images.
 18. A system for generating apseudo-CT image, comprising: a processor; a memory communicativelycoupled to the processor, the memory storing instructions that, whenexecuted by the processor, configure the processor to perform operationscomprising: receiving an Magnetic Resonance (MR) image of a patient;generating a Computerized Tomography (CT) value by applying a machinelearning model to a feature of the MR image, wherein the machinelearning model is trained to generate a pseudo-CT image for a receivedMR image based on an estimated parameter that represents a relationshipbetween an extracted feature of a training MR image and an extracted CTvalue corresponding to the extracted feature of the training MR image;and generating the pseudo-CT image based on the generated CT value. 19.The system of claim 18, wherein the received MR image is a first MRimage, further comprising operations for receiving a second MR image,wherein the first and second MR images are associated with different MRimaging protocols.
 20. The system of claim 19 further comprisingoperations for: obtaining one or more MR imaging protocol parametersassociated with the MR images; determining one or more intrinsic tissueparameters based on the MR imaging protocol parameters; generating oneor more tissue parameter maps for the one or more intrinsic tissueparameters; and generating one or more CT values by applying the machinelearning model to the one or more tissue parameter maps.
 21. The systemof claim 18, wherein the model is trained by a first set ofmulti-channel MR images having a first set of MR imaging protocolparameters and a second set of multi-channel MR images having a secondset of MR imaging protocol parameters that is different from the firstset of MR imaging protocol parameters.